Cross training helps humans and robots work better together

Cross-training techniques can help robots and humans work better together

Like many people, I spend most of my time worrying about the inevitable robot uprising. MIT is doing its bit to put off that day with its experiments in teaching robots and humans to work together peacefully. Using cross-training techniques, the researchers got robots and humans to swap jobs so they could see things from the others' point of view and carry out tasks more efficiently when working together.

Currently, research into teaching robots how to carry out a task as part of a team generally relies on the concept of an interactive reward. That is, if the robot performs a task to their liking, the operator gives it a positive response and if it doesn't, a negative response. The robot notes the responses and changes its behavior accordingly.

This is somewhat odd because humans don’t respond well to that sort of training. It’s a bit like playing a game of “hot and cold” without the more meaningful feedback that people prefer. According to MIT, recent military studies have shown that interactive rewards are an ineffective way of teaching people and perhaps the same is true of robots – at least, when they are working with people.

“People aren’t robots, they don’t do things the same way every single time,” said Julie Shah, an assistant professor of aeronautics and astronautics at MIT and head of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory. “And so there is a mismatch between the way we program robots to perform tasks in exactly the same way each time and what we need them to do if they are going to work in concert with people.”

Cross training is a more efficient training method that people do respond well to and which Shah suspected would be useful for robots as well. In cross training, workers swap jobs on a periodic basis. It’s a standard practice in small businesses where employees must wear more than one hat and where learning a new job is often a matter of getting thrown in at the deep end. It’s now being adopted by larger firms because it widens skill sets, reduces boredom and improves morale by giving workers more of a sense of growth and appreciation. It also allows for a better understanding of the other person’s job and how it’s done, which is the important thing as far as robots are concerned if they’re to work with people.

As applied by MIT, cross training involved designing a new robot algorithm to allow robots to learn from cross training by letting them learn by demonstration as well as interactive response. Demonstration is already being used in other robotic projects, such Georgia Tech’s work with a PR2 robot. The robots could watch their human partners and learn how they carried out their tasks.

The training saw two team humans and robot teams carrying out simulated tasks in a virtual environment, with one team acting as a control using conventional interactive feedback. Halfway through the session, the teams swapped jobs with the robots and completed the task. After the simulation, the teams carried out the tasks for real.

The results revealed that the humans and robots in the cross-training teams interacted 71 percent more often than the control group and the amount of time where the humans waited for the robots to finish a task decreased by 41 percent. The same was also true of the robots, whose algorithms recorded less uncertainty as to what the human would do next. On questionnaires, cross-training teammates also said that the robots carried out the test to their preferences more often and they had more trust in the robots than the control group.

“When the person trains the robot through reward it is one-way: The person says ‘good robot’ or the person says ‘bad robot,’ and it’s a very one-way passage of information,” Shah said. “But when you switch roles the person is better able to adapt to the robot’s capabilities and learn what it is likely to do, and so we think that it is adaptation on the person’s side that results in a better team performance.”

This cross training often has less to do with the robot than the human working with it. A robot doesn’t have any actual experiences or understanding. It’s really just a collection of sensors, databases and algorithms. However, if people start to anthropomorphize robots, it helps in working with them.

According to Kerstin Dautenhahn, a professor of artificial intelligence at the University of Hertfordshire, “People easily attribute human characteristics to a robot and treat it socially, so it is not entirely surprising that this transfer from the human-human domain to the human-robot domain not only made the teamwork more efficient, but also enhanced the experience for the participants, in terms of trusting the robot,” Dautenhahn says.